Decision Support for Stroke Rehabilitation Therapy via Describable Attribute-based Decision Trees

Does your therapist have an objective decision tree for how they approach your therapy? Or are they enamored of 'All strokes are different, all stroke recoveries are different'?
http://www.public.asu.edu/~pturaga/papers/StrokeDecisionTree.pdf

Vinay Venkataraman, Pavan Turaga, Nicole Lehrer, Michael Baran, Thanassis Rikakis, and Steven L. Wolf

Abstract

—This paper proposes a computational framework

for movement quality assessment using a decision tree model

that can potentially assist a physical therapist in a telereha-

bilitation context. Using a dataset of key kinematic attributes

collected from eight stroke survivors, we demonstrate that the

framework can be reliably used for movement quality assess-

ment of a reach-to-grasp cone task, an activity commonly used

in upper extremity stroke rehabilitation therapy. The proposed

framework is capable of providing movement quality scores that

are highly correlated to the ratings provided by therapists, who

used a custom rating rubric created by rehabilitation experts.

Our hypothesis is that a decision tree model could be easily

utilized by therapists as a potential assistive tool, especially in

evaluating movement quality on a large-scale dataset collected

during unsupervised rehabilitation (e.g., training at the home),

thereby reducing the time and cost of rehabilitation treatment.

 

6 more pages and some great math equations.